We have addressed the question of how can a single hub protein bind so many different partners. Numerous studies have sought differences between hubs and non-hubs to explain what makes a protein a hub and how a shared hub-binding site can be promiscuous, yet at the same time be specific. We suggest that the problem is largely non-existent and resides in the popular representation of protein interaction networks: protein products derived from a single gene, even if different, are clustered in maps into a single node. This leads to the impression that a single protein binds to a very large number of partners. In reality, it does not;rather, protein networks reflect the combination of multiple proteins, each with a distinct conformation. p53-response elements (p53-REs) are organized as two repeats of a palindromic DNA segment spaced by 0 to 20 base pairs (bp). Several experiments indicate that in the vast majority of the human p53-REs there are no spacers between the two repeats;those with spacers, particularly with sizes beyond two nucleotides, are rare. This raises the question of what it indicates about the factors determining the p53-RE genomic organization. Clearly, given the double helical DNA conformation, the orientation of two p53 core domain dimers with respect to each other will vary depending on the spacer size: a small spacer of 0 to 2 bps will lead to the closest p53 dimer-dimer orientation;a 10-bp spacer will locate the p53 dimers on the same DNA face but necessitate DNA looping;while a 5-bp spacer will position the p53 dimers on opposite DNA faces. Via conformational analysis we showed that when there are 0-2 bp spacers, p53-DNA binding is cooperative;however, cooperativity is greatly diminished when there are spacers with sizes beyond 2 bp. Cooperative binding is broadly recognized to be crucial for biological processes, including transcriptional regulation. Our results clearly indicated that cooperativity of the p53-DNA association dominates the genomic organization of the p53-REs, raising questions of the structural organization and functional roles of p53-REs with larger spacers. We further propose that a dynamic landscape scenario of p53 and p53-REs can better explain the selectivity of the degenerate p53-REs. Our conclusions bear on the evolutionary preference of the p53-RE organization and as such, are expected to have broad implications to other multimeric transcription factor response element organization. Inspection of protein-protein interaction maps illustrates that a hub protein can interact with a very large number of proteins, reaching tens and even hundreds. Since a single protein cannot interact with such a large number of partners at the same time, this presents a challenge: can we figure out which interactions can occur simultaneously and which are mutually excluded? Addressing this question adds a fourth dimension into interaction maps: that of time. Including the time dimension in structural networks is an immense asset;time dimensionality transforms network node-and-edge maps into cellular processes, assisting in the comprehension of cellular pathways and their regulation. While the time dimensionality can be further enhanced by linking protein complexes to time series of mRNA expression data, current robust, network experimental data are lacking. Here we outline how, using structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks;in predicting which interactions can and cannot co-exist;and in obtaining concrete predictions consistent with experiment. As an example, we presented p53-linked processes. A key step in drug development is identification of the protein to be targeted and its topological cellular network location and interactions. These relate to the information flow in disease-causing events and to medication effects. Information flow involves a cascade of binding or covalent modification processes;with each step affected by previous ones. Proteins are flexible, and information flows via dynamic changes of the distributions of their conformational ensembles;and molecular recognition is largely determined by these changes. Drug discovery often focuses on signaling proteins, at the cross-roads of cellular networks. Signaling proteins have multiple partners binding through shared binding sites. We have highlighted these shared binding sites. The reviewed data suggest that partners binding at these sites appear to interact via different energetically-dominant hot spot residues and that despite the dynamic changes in the distribution of the conformational ensembles, the hot spot conformations are retained in their pre-organized states. A new amino acid has been designed as a replacement for arginine (Arg, R) to protect the tumor-homing pentapeptide CREKA (Cys-Arg-Glu-Lys-Ala) from proteases. This amino acid, denoted (Pro)hArg, is characterized by a proline skeleton bearing a specifically oriented guanidinium side chain. This residue combines the ability of Pro to induce turn-like conformations with the Arg side-chain functionality. The conformational profile of the CREKA analogue incorporating this Arg substitute has been investigated by a combination of simulated annealing and molecular dynamics. Comparison of the results with those previously obtained for the natural CREKA shows that (Pro)hArg significantly reduces the conformational flexibility of the peptide. Although some changes are observed in the backbone...backbone and side-chain...side-chain interactions, the modified peptide exhibits a strong tendency to accommodate turn conformations centered at the (Pro)hArg residue and the overall shape of the molecule in the lowest energy conformations characterized for the natural and the modified peptides exhibit a high degree of similarity. In particular, the turn orients the backbone such that the Arg, Glu, and Lys side chains face the same side of the molecule, which is considered important for bioactivity. Our results suggested that replacement of Arg by (Pro)hArg in CREKA may be useful in providing resistance against proteolytic enzymes while retaining conformational features which are essential for tumor-homing activity. Peptides and proteins find an ever-increasing number of applications in the biomedical and materials engineering fields. The use of non-proteinogenic amino acids endowed with diverse physicochemical and structural features opens the possibility to design proteins and peptides with novel properties and functions. Moreover, non-proteinogenic residues are particularly useful to control the three-dimensional arrangement of peptidic chains, which is a crucial issue for most applications. However, information regarding such amino acids--also called non-coded, non-canonical, or non-standard--is usually scattered among publications specialized in quite diverse fields as well as in patents. Making all these data useful to the scientific community requires new tools and a framework for their assembly and coherent organization. We have successfully compiled, organized, and built a database (NCAD, Non-Coded Amino acids Database) containing information about the intrinsic conformational preferences of non-proteinogenic residues determined by quantum mechanical calculations, as well as bibliographic information about their synthesis, physical and spectroscopic characterization, conformational propensities established experimentally, and applications. The architecture of the database is presented in this work together with the first family of non-coded residues included, namely, alpha-tetrasubstituted alpha-amino acids. Furthermore, the NCAD usefulness is demonstrated through a test-case application example.